Bibliographic record
Abstract
Purpose The purpose of this paper is to test the merits of the view that the English language has emerged as the dominant language in international business. If there is merit to this view, then the ability to speak English and its role as a lingua franca in the global economy would imply that countries which have English as an official language should have a benefit over non‐English‐speaking countriesvis‐à‐vistheir abilities to undertake international business. Design/methodology/approach Within an augmented gravity model framework, the importance of the English language in explaining bilateral foreign direct investment (FDI) data within the OECD is tested. In addition to English, all other common official languages within the OECD are also tested. Furthermore, the linguistic distance to English is used to test whether closeness of languages to English enhance international business activity. Findings The results indicate that English‐speaking countries within the OECD do have a benefit that comes with the English language. Furthermore, countries whose official languages are linguistically close to English benefit from the special role played by the English language. These results therefore highlight the importance of the English language in deploying multinational strategies, even in countries whose official language is not English. Research limitations/implications These results therefore indicate the importance of the English language in international business. As such, having a proficiency with English within any corporation should enhance that corporation's ability to engage in international business. Originality/value Sharing a common language with FDI partners enhances the ability to communicate, and hence enhances FDI between the countries. This paper extends this evidence to show that when the common language is English, the common language effect is strongest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".